Instructions to use P1n3/sdg-detector-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use P1n3/sdg-detector-sft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="P1n3/sdg-detector-sft")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("P1n3/sdg-detector-sft")
model = AutoModelForMultimodalLM.from_pretrained("P1n3/sdg-detector-sft")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use P1n3/sdg-detector-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "P1n3/sdg-detector-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "P1n3/sdg-detector-sft",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/P1n3/sdg-detector-sft

SGLang

How to use P1n3/sdg-detector-sft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "P1n3/sdg-detector-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "P1n3/sdg-detector-sft",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "P1n3/sdg-detector-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "P1n3/sdg-detector-sft",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use P1n3/sdg-detector-sft with Docker Model Runner:
```
docker model run hf.co/P1n3/sdg-detector-sft
```

SDG Detector — SFT Stage

Stage-1 supervised fine-tuning of Qwen/Qwen3-VL-4B-Instruct on the SDG-30K training split. This checkpoint backs the "SDG (SFT)" row of Table 1 in the SDG paper.

The model emits structured defect-grounding output:

<think>
[Caption Understanding -> Visual Analysis -> Defect Spotting -> Localization]
</think>
<answer>
[
  {"box_2d": [y0, x0, y1, x1], "label": "artifact" | "misalignment", "desc": "..."},
  ...
]
</answer>

box_2d uses the [0, 1000] normalized convention (top, left, bottom, right).

Training summary

field	value
base model	`Qwen/Qwen3-VL-4B-Instruct`
training data	SDG-30K train split (~85,770 prompt-response pairs after CoT distillation + jitter)
epochs	1 effective (5,360 steps × effective batch 16)
learning rate	3.0e-5, cosine, 5% warmup
coord jitter	±10 px, per-epoch resampling
vision encoder	frozen
precision	bf16
hardware	16 × A100-80G (2 nodes × 8)
optimizer state	not redistributed (release-only checkpoint)

Quick start

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

ckpt = "P1n3/sdg-detector-sft"
processor = AutoProcessor.from_pretrained(ckpt)
model = AutoModelForImageTextToText.from_pretrained(
    ckpt, dtype=torch.bfloat16, device_map="auto",
)

The exact prompt template lives in the supplementary archive at sdg_detector/train/constants.py (question_template_registry).

Stage-2 GRPO Checkpoint

The Stage-2 GRPO detector is released separately as a merged full checkpoint: P1n3/sdg-detector-grpo. It can be loaded directly with transformers and should not be attached as a PEFT adapter.

License

cc-by-nc-4.0. Derivative of Qwen/Qwen3-VL-4B-Instruct (Apache-2.0). Released for non-commercial research use only.

Citation

@article{zhang2026and,
  title={Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback},
  author={Zhang, Huaisong and Yu, Hao and Zhang, Yuxuan and Wang, Jiahe and Chen, Xinrui and Cao, Haoxiang and Lu, Feng and Zhang, Wendong and Yu, Changqian and Yuan, Chun},
  journal={arXiv preprint arXiv:2606.06113},
  year={2026}
}